Overview

Dataset statistics

Number of variables16
Number of observations8322
Missing cells20217
Missing cells (%)15.2%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory1.0 MiB
Average record size in memory128.0 B

Variable types

Numeric7
Text7
DateTime2

Alerts

Size is highly overall correlated with Total Time and 1 other fieldsHigh correlation
Total Time is highly overall correlated with SizeHigh correlation
Bit Rate is highly overall correlated with SizeHigh correlation
Track Number has 319 (3.8%) missing valuesMissing
Track Count has 6971 (83.8%) missing valuesMissing
Composer has 6297 (75.7%) missing valuesMissing
Publisher has 6614 (79.5%) missing valuesMissing
Track ID has unique valuesUnique

Reproduction

Analysis started2023-11-04 08:16:46.871059
Analysis finished2023-11-04 08:16:49.893270
Duration3.02 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

Track ID
Real number (ℝ)

UNIQUE 

Distinct8322
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean5856.5833
Minimum0
Maximum11434
Zeros1
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size65.1 KiB
2023-11-04T02:16:49.929685image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile759.05
Q13079.25
median5606.5
Q38811.75
95-th percentile10768.95
Maximum11434
Range11434
Interquartile range (IQR)5732.5

Descriptive statistics

Standard deviation3253.8588
Coefficient of variation (CV)0.55558995
Kurtosis-1.2452143
Mean5856.5833
Median Absolute Deviation (MAD)2935
Skewness0.014942539
Sum48738486
Variance10587597
MonotonicityStrictly increasing
2023-11-04T02:16:49.985191image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 1
 
< 0.1%
7923 1
 
< 0.1%
7921 1
 
< 0.1%
7920 1
 
< 0.1%
7919 1
 
< 0.1%
7918 1
 
< 0.1%
7917 1
 
< 0.1%
7916 1
 
< 0.1%
7915 1
 
< 0.1%
7914 1
 
< 0.1%
Other values (8312) 8312
99.9%
ValueCountFrequency (%)
0 1
< 0.1%
1 1
< 0.1%
2 1
< 0.1%
3 1
< 0.1%
4 1
< 0.1%
5 1
< 0.1%
6 1
< 0.1%
7 1
< 0.1%
8 1
< 0.1%
9 1
< 0.1%
ValueCountFrequency (%)
11434 1
< 0.1%
11433 1
< 0.1%
11432 1
< 0.1%
11431 1
< 0.1%
11430 1
< 0.1%
11429 1
< 0.1%
11428 1
< 0.1%
11427 1
< 0.1%
11426 1
< 0.1%
11425 1
< 0.1%

Name
Text

Distinct7228
Distinct (%)86.9%
Missing0
Missing (%)0.0%
Memory size65.1 KiB
2023-11-04T02:16:50.192598image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Length

Max length100
Median length71
Mean length15.752944
Min length1

Characters and Unicode

Total characters131096
Distinct characters146
Distinct categories16 ?
Distinct scripts5 ?
Distinct blocks7 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique6406 ?
Unique (%)77.0%

Sample

1st rowA Story Of Boy Meets Girl
2nd rowUs
3rd rowThere Is A Light That Never Goes Out
4th rowBad Kids
5th rowPlease, Please, Please, Let Me Get What I Want
ValueCountFrequency (%)
the 1272
 
5.3%
of 561
 
2.3%
a 299
 
1.2%
you 294
 
1.2%
in 281
 
1.2%
278
 
1.2%
to 249
 
1.0%
i 226
 
0.9%
me 193
 
0.8%
and 190
 
0.8%
Other values (6715) 20281
84.1%
2023-11-04T02:16:50.478219image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
15805
 
12.1%
e 12800
 
9.8%
o 7830
 
6.0%
a 7582
 
5.8%
n 6819
 
5.2%
i 6697
 
5.1%
r 6418
 
4.9%
t 6077
 
4.6%
s 4877
 
3.7%
l 4486
 
3.4%
Other values (136) 51705
39.4%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 88482
67.5%
Uppercase Letter 22737
 
17.3%
Space Separator 15805
 
12.1%
Other Punctuation 1481
 
1.1%
Decimal Number 814
 
0.6%
Close Punctuation 699
 
0.5%
Open Punctuation 693
 
0.5%
Dash Punctuation 302
 
0.2%
Other Letter 43
 
< 0.1%
Math Symbol 15
 
< 0.1%
Other values (6) 25
 
< 0.1%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
e 12800
14.5%
o 7830
 
8.8%
a 7582
 
8.6%
n 6819
 
7.7%
i 6697
 
7.6%
r 6418
 
7.3%
t 6077
 
6.9%
s 4877
 
5.5%
l 4486
 
5.1%
h 3833
 
4.3%
Other values (30) 21063
23.8%
Uppercase Letter
ValueCountFrequency (%)
T 2394
 
10.5%
S 1983
 
8.7%
M 1480
 
6.5%
B 1325
 
5.8%
A 1324
 
5.8%
D 1255
 
5.5%
L 1186
 
5.2%
C 1165
 
5.1%
I 1159
 
5.1%
W 1068
 
4.7%
Other values (23) 8398
36.9%
Other Letter
ValueCountFrequency (%)
の 5
 
11.6%
き 3
 
7.0%
ド 3
 
7.0%
ロ 2
 
4.7%
ナ 2
 
4.7%
イ 2
 
4.7%
グ 2
 
4.7%
マ 2
 
4.7%
ら 2
 
4.7%
議 1
 
2.3%
Other values (19) 19
44.2%
Other Punctuation
ValueCountFrequency (%)
' 573
38.7%
. 337
22.8%
, 207
 
14.0%
& 78
 
5.3%
! 76
 
5.1%
/ 57
 
3.8%
? 43
 
2.9%
* 37
 
2.5%
: 34
 
2.3%
# 19
 
1.3%
Other values (5) 20
 
1.4%
Decimal Number
ValueCountFrequency (%)
1 180
22.1%
0 169
20.8%
2 146
17.9%
8 66
 
8.1%
3 63
 
7.7%
9 51
 
6.3%
4 49
 
6.0%
5 31
 
3.8%
6 30
 
3.7%
7 29
 
3.6%
Math Symbol
ValueCountFrequency (%)
~ 6
40.0%
+ 4
26.7%
= 3
20.0%
～ 2
 
13.3%
Close Punctuation
ValueCountFrequency (%)
) 527
75.4%
] 172
 
24.6%
Open Punctuation
ValueCountFrequency (%)
( 521
75.2%
[ 172
 
24.8%
Modifier Symbol
ValueCountFrequency (%)
` 5
83.3%
´ 1
 
16.7%
Final Punctuation
ValueCountFrequency (%)
’ 5
55.6%
” 4
44.4%
Currency Symbol
ValueCountFrequency (%)
€ 2
66.7%
$ 1
33.3%
Space Separator
ValueCountFrequency (%)
15805
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 302
100.0%
Initial Punctuation
ValueCountFrequency (%)
“ 4
100.0%
Connector Punctuation
ValueCountFrequency (%)
_ 2
100.0%
Other Symbol
ValueCountFrequency (%)
® 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 111219
84.8%
Common 19834
 
15.1%
Hiragana 24
 
< 0.1%
Katakana 15
 
< 0.1%
Han 4
 
< 0.1%

Most frequent character per script

Latin
ValueCountFrequency (%)
e 12800
 
11.5%
o 7830
 
7.0%
a 7582
 
6.8%
n 6819
 
6.1%
i 6697
 
6.0%
r 6418
 
5.8%
t 6077
 
5.5%
s 4877
 
4.4%
l 4486
 
4.0%
h 3833
 
3.4%
Other values (63) 43800
39.4%
Common
ValueCountFrequency (%)
15805
79.7%
' 573
 
2.9%
) 527
 
2.7%
( 521
 
2.6%
. 337
 
1.7%
- 302
 
1.5%
, 207
 
1.0%
1 180
 
0.9%
[ 172
 
0.9%
] 172
 
0.9%
Other values (34) 1038
 
5.2%
Hiragana
ValueCountFrequency (%)
の 5
20.8%
き 3
12.5%
ら 2
 
8.3%
り 1
 
4.2%
ふ 1
 
4.2%
し 1
 
4.2%
ぎ 1
 
4.2%
く 1
 
4.2%
に 1
 
4.2%
ど 1
 
4.2%
Other values (7) 7
29.2%
Katakana
ValueCountFrequency (%)
ド 3
20.0%
ロ 2
13.3%
ナ 2
13.3%
イ 2
13.3%
グ 2
13.3%
マ 2
13.3%
ル 1
 
6.7%
リ 1
 
6.7%
Han
ValueCountFrequency (%)
議 1
25.0%
不 1
25.0%
思 1
25.0%
国 1
25.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 130892
99.8%
None 146
 
0.1%
Hiragana 24
 
< 0.1%
Katakana 15
 
< 0.1%
Punctuation 13
 
< 0.1%
CJK 4
 
< 0.1%
Currency Symbols 2
 
< 0.1%

Most frequent character per block

ASCII
ValueCountFrequency (%)
15805
 
12.1%
e 12800
 
9.8%
o 7830
 
6.0%
a 7582
 
5.8%
n 6819
 
5.2%
i 6697
 
5.1%
r 6418
 
4.9%
t 6077
 
4.6%
s 4877
 
3.7%
l 4486
 
3.4%
Other values (77) 51501
39.3%
None
ValueCountFrequency (%)
ó 21
14.4%
ñ 17
11.6%
á 13
8.9%
é 12
8.2%
à 11
 
7.5%
ä 10
 
6.8%
å 9
 
6.2%
ö 9
 
6.2%
ü 8
 
5.5%
í 7
 
4.8%
Other values (16) 29
19.9%
Hiragana
ValueCountFrequency (%)
の 5
20.8%
き 3
12.5%
ら 2
 
8.3%
り 1
 
4.2%
ふ 1
 
4.2%
し 1
 
4.2%
ぎ 1
 
4.2%
く 1
 
4.2%
に 1
 
4.2%
ど 1
 
4.2%
Other values (7) 7
29.2%
Punctuation
ValueCountFrequency (%)
’ 5
38.5%
“ 4
30.8%
” 4
30.8%
Katakana
ValueCountFrequency (%)
ド 3
20.0%
ロ 2
13.3%
ナ 2
13.3%
イ 2
13.3%
グ 2
13.3%
マ 2
13.3%
ル 1
 
6.7%
リ 1
 
6.7%
Currency Symbols
ValueCountFrequency (%)
€ 2
100.0%
CJK
ValueCountFrequency (%)
議 1
25.0%
不 1
25.0%
思 1
25.0%
国 1
25.0%

Artist
Text

Distinct1640
Distinct (%)19.7%
Missing0
Missing (%)0.0%
Memory size65.1 KiB
2023-11-04T02:16:50.669641image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Length

Max length84
Median length48
Mean length11.155852
Min length2

Characters and Unicode

Total characters92839
Distinct characters82
Distinct categories9 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique944 ?
Unique (%)11.3%

Sample

1st rowMychael Danna & Rob Simonsen
2nd rowRegina Spektor
3rd rowThe Smiths
4th rowBlack Lips
5th rowThe Smiths
ValueCountFrequency (%)
the 1226
 
7.7%
pink 341
 
2.1%
floyd 341
 
2.1%
mode 292
 
1.8%
depeche 292
 
1.8%
metallica 282
 
1.8%
iron 242
 
1.5%
maiden 240
 
1.5%
of 204
 
1.3%
misfits 164
 
1.0%
Other values (2341) 12361
77.3%
2023-11-04T02:16:50.917451image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
e 10107
 
10.9%
7663
 
8.3%
a 6357
 
6.8%
i 5499
 
5.9%
n 5283
 
5.7%
o 5162
 
5.6%
r 4345
 
4.7%
s 4186
 
4.5%
l 4116
 
4.4%
t 3730
 
4.0%
Other values (72) 36391
39.2%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 67996
73.2%
Uppercase Letter 16232
 
17.5%
Space Separator 7663
 
8.3%
Other Punctuation 674
 
0.7%
Decimal Number 214
 
0.2%
Dash Punctuation 42
 
< 0.1%
Math Symbol 16
 
< 0.1%
Open Punctuation 1
 
< 0.1%
Close Punctuation 1
 
< 0.1%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
e 10107
14.9%
a 6357
 
9.3%
i 5499
 
8.1%
n 5283
 
7.8%
o 5162
 
7.6%
r 4345
 
6.4%
s 4186
 
6.2%
l 4116
 
6.1%
t 3730
 
5.5%
h 3191
 
4.7%
Other values (24) 16020
23.6%
Uppercase Letter
ValueCountFrequency (%)
M 2137
13.2%
T 1853
 
11.4%
S 1353
 
8.3%
D 1096
 
6.8%
F 1039
 
6.4%
P 982
 
6.0%
A 839
 
5.2%
C 810
 
5.0%
L 733
 
4.5%
B 606
 
3.7%
Other values (16) 4784
29.5%
Decimal Number
ValueCountFrequency (%)
5 71
33.2%
0 59
27.6%
2 25
 
11.7%
1 16
 
7.5%
8 15
 
7.0%
3 13
 
6.1%
6 7
 
3.3%
9 5
 
2.3%
7 3
 
1.4%
Other Punctuation
ValueCountFrequency (%)
. 266
39.5%
' 130
19.3%
& 99
 
14.7%
, 64
 
9.5%
! 60
 
8.9%
/ 36
 
5.3%
: 18
 
2.7%
? 1
 
0.1%
Space Separator
ValueCountFrequency (%)
7663
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 42
100.0%
Math Symbol
ValueCountFrequency (%)
+ 16
100.0%
Open Punctuation
ValueCountFrequency (%)
[ 1
100.0%
Close Punctuation
ValueCountFrequency (%)
] 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 84228
90.7%
Common 8611
 
9.3%

Most frequent character per script

Latin
ValueCountFrequency (%)
e 10107
 
12.0%
a 6357
 
7.5%
i 5499
 
6.5%
n 5283
 
6.3%
o 5162
 
6.1%
r 4345
 
5.2%
s 4186
 
5.0%
l 4116
 
4.9%
t 3730
 
4.4%
h 3191
 
3.8%
Other values (50) 32252
38.3%
Common
ValueCountFrequency (%)
7663
89.0%
. 266
 
3.1%
' 130
 
1.5%
& 99
 
1.1%
5 71
 
0.8%
, 64
 
0.7%
! 60
 
0.7%
0 59
 
0.7%
- 42
 
0.5%
/ 36
 
0.4%
Other values (12) 121
 
1.4%

Most occurring blocks

ValueCountFrequency (%)
ASCII 92518
99.7%
None 321
 
0.3%

Most frequent character per block

ASCII
ValueCountFrequency (%)
e 10107
 
10.9%
7663
 
8.3%
a 6357
 
6.9%
i 5499
 
5.9%
n 5283
 
5.7%
o 5162
 
5.6%
r 4345
 
4.7%
s 4186
 
4.5%
l 4116
 
4.4%
t 3730
 
4.0%
Other values (64) 36070
39.0%
None
ValueCountFrequency (%)
é 94
29.3%
ä 92
28.7%
å 60
18.7%
ö 36
 
11.2%
ó 28
 
8.7%
á 5
 
1.6%
í 5
 
1.6%
ñ 1
 
0.3%

Album Artist
Text

Distinct643
Distinct (%)7.7%
Missing0
Missing (%)0.0%
Memory size65.1 KiB
2023-11-04T02:16:51.110506image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Length

Max length84
Median length48
Mean length11.630978
Min length2

Characters and Unicode

Total characters96793
Distinct characters83
Distinct categories9 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique231 ?
Unique (%)2.8%

Sample

1st rowMychael Danna & Rob Simonsen
2nd rowRegina Spektor
3rd rowThe Smiths
4th rowBlack Lips
5th rowThe Smiths
ValueCountFrequency (%)
various 1678
 
10.8%
artists 1678
 
10.8%
the 832
 
5.3%
floyd 341
 
2.2%
pink 341
 
2.2%
depeche 292
 
1.9%
mode 292
 
1.9%
metallica 282
 
1.8%
iron 240
 
1.5%
maiden 240
 
1.5%
Other values (999) 9362
60.1%
2023-11-04T02:16:51.358581image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
s 8147
 
8.4%
e 7880
 
8.1%
i 7681
 
7.9%
7256
 
7.5%
a 6574
 
6.8%
r 6518
 
6.7%
t 6156
 
6.4%
o 5725
 
5.9%
n 4207
 
4.3%
l 3255
 
3.4%
Other values (73) 33394
34.5%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 72917
75.3%
Uppercase Letter 15788
 
16.3%
Space Separator 7256
 
7.5%
Other Punctuation 596
 
0.6%
Decimal Number 184
 
0.2%
Dash Punctuation 34
 
< 0.1%
Math Symbol 16
 
< 0.1%
Open Punctuation 1
 
< 0.1%
Close Punctuation 1
 
< 0.1%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
s 8147
11.2%
e 7880
10.8%
i 7681
10.5%
a 6574
9.0%
r 6518
8.9%
t 6156
8.4%
o 5725
7.9%
n 4207
 
5.8%
l 3255
 
4.5%
u 2927
 
4.0%
Other values (24) 13847
19.0%
Uppercase Letter
ValueCountFrequency (%)
A 2287
14.5%
M 1883
11.9%
V 1803
11.4%
T 1273
 
8.1%
S 991
 
6.3%
D 905
 
5.7%
F 849
 
5.4%
P 812
 
5.1%
L 571
 
3.6%
C 571
 
3.6%
Other values (16) 3843
24.3%
Other Punctuation
ValueCountFrequency (%)
. 276
46.3%
' 112
18.8%
& 64
 
10.7%
, 48
 
8.1%
! 39
 
6.5%
/ 25
 
4.2%
: 18
 
3.0%
@ 13
 
2.2%
? 1
 
0.2%
Decimal Number
ValueCountFrequency (%)
5 67
36.4%
0 45
24.5%
2 19
 
10.3%
4 13
 
7.1%
1 12
 
6.5%
3 11
 
6.0%
8 9
 
4.9%
6 5
 
2.7%
7 3
 
1.6%
Space Separator
ValueCountFrequency (%)
7256
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 34
100.0%
Math Symbol
ValueCountFrequency (%)
+ 16
100.0%
Open Punctuation
ValueCountFrequency (%)
[ 1
100.0%
Close Punctuation
ValueCountFrequency (%)
] 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 88705
91.6%
Common 8088
 
8.4%

Most frequent character per script

Latin
ValueCountFrequency (%)
s 8147
 
9.2%
e 7880
 
8.9%
i 7681
 
8.7%
a 6574
 
7.4%
r 6518
 
7.3%
t 6156
 
6.9%
o 5725
 
6.5%
n 4207
 
4.7%
l 3255
 
3.7%
u 2927
 
3.3%
Other values (50) 29635
33.4%
Common
ValueCountFrequency (%)
7256
89.7%
. 276
 
3.4%
' 112
 
1.4%
5 67
 
0.8%
& 64
 
0.8%
, 48
 
0.6%
0 45
 
0.6%
! 39
 
0.5%
- 34
 
0.4%
/ 25
 
0.3%
Other values (13) 122
 
1.5%

Most occurring blocks

ValueCountFrequency (%)
ASCII 96501
99.7%
None 292
 
0.3%

Most frequent character per block

ASCII
ValueCountFrequency (%)
s 8147
 
8.4%
e 7880
 
8.2%
i 7681
 
8.0%
7256
 
7.5%
a 6574
 
6.8%
r 6518
 
6.8%
t 6156
 
6.4%
o 5725
 
5.9%
n 4207
 
4.4%
l 3255
 
3.4%
Other values (65) 33102
34.3%
None
ValueCountFrequency (%)
é 94
32.2%
ä 92
31.5%
å 60
20.5%
ó 26
 
8.9%
ö 9
 
3.1%
á 5
 
1.7%
í 4
 
1.4%
ü 2
 
0.7%

Album
Text

Distinct776
Distinct (%)9.3%
Missing16
Missing (%)0.2%
Memory size65.1 KiB
2023-11-04T02:16:51.569654image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Length

Max length85
Median length44
Mean length21.712256
Min length1

Characters and Unicode

Total characters180342
Distinct characters89
Distinct categories10 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique119 ?
Unique (%)1.4%

Sample

1st row(500) Days Of Summer
2nd row(500) Days Of Summer
3rd row(500) Days Of Summer
4th row(500) Days Of Summer
5th row(500) Days Of Summer
ValueCountFrequency (%)
playlist 1657
 
5.6%
the 1624
 
5.5%
rock 1537
 
5.2%
indie 1472
 
4.9%
2008 1242
 
4.2%
of 778
 
2.6%
483
 
1.6%
january 334
 
1.1%
disc 319
 
1.1%
live 299
 
1.0%
Other values (1215) 20044
67.3%
2023-11-04T02:16:51.837965image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
21542
 
11.9%
e 14468
 
8.0%
i 10046
 
5.6%
a 9000
 
5.0%
o 8248
 
4.6%
l 8038
 
4.5%
n 7746
 
4.3%
t 7696
 
4.3%
s 7403
 
4.1%
r 6638
 
3.7%
Other values (79) 79517
44.1%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 113502
62.9%
Uppercase Letter 26476
 
14.7%
Space Separator 21542
 
11.9%
Decimal Number 9976
 
5.5%
Other Punctuation 3111
 
1.7%
Open Punctuation 2503
 
1.4%
Close Punctuation 2461
 
1.4%
Dash Punctuation 716
 
0.4%
Math Symbol 47
 
< 0.1%
Currency Symbol 8
 
< 0.1%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
e 14468
12.7%
i 10046
 
8.9%
a 9000
 
7.9%
o 8248
 
7.3%
l 8038
 
7.1%
n 7746
 
6.8%
t 7696
 
6.8%
s 7403
 
6.5%
r 6638
 
5.8%
c 4874
 
4.3%
Other values (25) 29345
25.9%
Uppercase Letter
ValueCountFrequency (%)
R 2405
 
9.1%
I 2374
 
9.0%
P 2367
 
8.9%
T 2162
 
8.2%
S 1956
 
7.4%
D 1659
 
6.3%
A 1407
 
5.3%
B 1405
 
5.3%
M 1396
 
5.3%
C 1201
 
4.5%
Other values (16) 8144
30.8%
Decimal Number
ValueCountFrequency (%)
0 3959
39.7%
2 2264
22.7%
8 1477
 
14.8%
1 803
 
8.0%
9 601
 
6.0%
7 259
 
2.6%
4 234
 
2.3%
3 166
 
1.7%
6 141
 
1.4%
5 72
 
0.7%
Other Punctuation
ValueCountFrequency (%)
: 1733
55.7%
' 546
 
17.6%
/ 313
 
10.1%
. 195
 
6.3%
, 114
 
3.7%
& 107
 
3.4%
! 69
 
2.2%
? 33
 
1.1%
@ 1
 
< 0.1%
Open Punctuation
ValueCountFrequency (%)
( 2157
86.2%
[ 346
 
13.8%
Close Punctuation
ValueCountFrequency (%)
) 2115
85.9%
] 346
 
14.1%
Math Symbol
ValueCountFrequency (%)
+ 36
76.6%
= 11
 
23.4%
Space Separator
ValueCountFrequency (%)
21542
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 716
100.0%
Currency Symbol
ValueCountFrequency (%)
$ 8
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 139978
77.6%
Common 40364
 
22.4%

Most frequent character per script

Latin
ValueCountFrequency (%)
e 14468
 
10.3%
i 10046
 
7.2%
a 9000
 
6.4%
o 8248
 
5.9%
l 8038
 
5.7%
n 7746
 
5.5%
t 7696
 
5.5%
s 7403
 
5.3%
r 6638
 
4.7%
c 4874
 
3.5%
Other values (51) 55821
39.9%
Common
ValueCountFrequency (%)
21542
53.4%
0 3959
 
9.8%
2 2264
 
5.6%
( 2157
 
5.3%
) 2115
 
5.2%
: 1733
 
4.3%
8 1477
 
3.7%
1 803
 
2.0%
- 716
 
1.8%
9 601
 
1.5%
Other values (18) 2997
 
7.4%

Most occurring blocks

ValueCountFrequency (%)
ASCII 180056
99.8%
None 286
 
0.2%

Most frequent character per block

ASCII
ValueCountFrequency (%)
21542
 
12.0%
e 14468
 
8.0%
i 10046
 
5.6%
a 9000
 
5.0%
o 8248
 
4.6%
l 8038
 
4.5%
n 7746
 
4.3%
t 7696
 
4.3%
s 7403
 
4.1%
r 6638
 
3.7%
Other values (70) 79231
44.0%
None
ValueCountFrequency (%)
ü 167
58.4%
ó 27
 
9.4%
é 24
 
8.4%
å 17
 
5.9%
í 15
 
5.2%
á 14
 
4.9%
ä 10
 
3.5%
ø 9
 
3.1%
ö 3
 
1.0%

Genre
Text

Distinct131
Distinct (%)1.6%
Missing0
Missing (%)0.0%
Memory size65.1 KiB
2023-11-04T02:16:52.002976image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Length

Max length49
Median length42
Mean length6.8930546
Min length3

Characters and Unicode

Total characters57364
Distinct characters56
Distinct categories6 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique36 ?
Unique (%)0.4%

Sample

1st rowSoundtrack
2nd rowSoundtrack
3rd rowSoundtrack
4th rowSoundtrack
5th rowSoundtrack
ValueCountFrequency (%)
indie 2115
20.1%
rock 2100
20.0%
metal 1273
12.1%
punk 507
 
4.8%
pop 455
 
4.3%
electronic 398
 
3.8%
hard 336
 
3.2%
alternative 309
 
2.9%
293
 
2.8%
heavy 291
 
2.8%
Other values (117) 2429
23.1%
2023-11-04T02:16:52.207906image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
e 5977
 
10.4%
n 4174
 
7.3%
o 4002
 
7.0%
c 3942
 
6.9%
a 3581
 
6.2%
i 3553
 
6.2%
t 3145
 
5.5%
k 3115
 
5.4%
d 2877
 
5.0%
l 2837
 
4.9%
Other values (46) 20161
35.1%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 44230
77.1%
Uppercase Letter 10429
 
18.2%
Space Separator 2184
 
3.8%
Other Punctuation 392
 
0.7%
Dash Punctuation 127
 
0.2%
Decimal Number 2
 
< 0.1%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
e 5977
13.5%
n 4174
9.4%
o 4002
9.0%
c 3942
8.9%
a 3581
8.1%
i 3553
8.0%
t 3145
7.1%
k 3115
7.0%
d 2877
6.5%
l 2837
6.4%
Other values (17) 7027
15.9%
Uppercase Letter
ValueCountFrequency (%)
R 2303
22.1%
I 2161
20.7%
M 1310
12.6%
P 1049
10.1%
H 851
 
8.2%
E 500
 
4.8%
A 433
 
4.2%
S 289
 
2.8%
B 274
 
2.6%
D 270
 
2.6%
Other values (11) 989
9.5%
Other Punctuation
ValueCountFrequency (%)
/ 209
53.3%
& 175
44.6%
: 5
 
1.3%
, 3
 
0.8%
Decimal Number
ValueCountFrequency (%)
4 1
50.0%
0 1
50.0%
Space Separator
ValueCountFrequency (%)
2184
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 127
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 54659
95.3%
Common 2705
 
4.7%

Most frequent character per script

Latin
ValueCountFrequency (%)
e 5977
 
10.9%
n 4174
 
7.6%
o 4002
 
7.3%
c 3942
 
7.2%
a 3581
 
6.6%
i 3553
 
6.5%
t 3145
 
5.8%
k 3115
 
5.7%
d 2877
 
5.3%
l 2837
 
5.2%
Other values (38) 17456
31.9%
Common
ValueCountFrequency (%)
2184
80.7%
/ 209
 
7.7%
& 175
 
6.5%
- 127
 
4.7%
: 5
 
0.2%
, 3
 
0.1%
4 1
 
< 0.1%
0 1
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 57336
> 99.9%
None 28
 
< 0.1%

Most frequent character per block

ASCII
ValueCountFrequency (%)
e 5977
 
10.4%
n 4174
 
7.3%
o 4002
 
7.0%
c 3942
 
6.9%
a 3581
 
6.2%
i 3553
 
6.2%
t 3145
 
5.5%
k 3115
 
5.4%
d 2877
 
5.0%
l 2837
 
4.9%
Other values (43) 20133
35.1%
None
ValueCountFrequency (%)
ó 15
53.6%
ñ 12
42.9%
ú 1
 
3.6%

Size
Real number (ℝ)

HIGH CORRELATION 

Distinct8055
Distinct (%)96.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean7264031.1
Minimum369918
Maximum1.4405505 × 108
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size65.1 KiB
2023-11-04T02:16:52.280317image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum369918
5-th percentile2646128.9
Q14779084.2
median6473582
Q38534680.5
95-th percentile13868704
Maximum1.4405505 × 108
Range1.4368513 × 108
Interquartile range (IQR)3755596.2

Descriptive statistics

Standard deviation4903432.8
Coefficient of variation (CV)0.67502917
Kurtosis119.63265
Mean7264031.1
Median Absolute Deviation (MAD)1854967
Skewness7.2749798
Sum6.0451266 × 1010
Variance2.4043654 × 1013
MonotonicityNot monotonic
2023-11-04T02:16:52.333385image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
5453824 3
 
< 0.1%
8508815 3
 
< 0.1%
5072896 3
 
< 0.1%
6640108 3
 
< 0.1%
5874861 3
 
< 0.1%
7142717 3
 
< 0.1%
7006208 3
 
< 0.1%
8943616 3
 
< 0.1%
11419648 3
 
< 0.1%
5363712 3
 
< 0.1%
Other values (8045) 8292
99.6%
ValueCountFrequency (%)
369918 1
< 0.1%
386415 1
< 0.1%
429401 1
< 0.1%
439015 1
< 0.1%
452799 1
< 0.1%
465533 1
< 0.1%
481408 1
< 0.1%
486236 1
< 0.1%
493341 1
< 0.1%
497528 1
< 0.1%
ValueCountFrequency (%)
144055047 1
< 0.1%
89555478 1
< 0.1%
77926209 2
< 0.1%
71917970 1
< 0.1%
67541334 1
< 0.1%
66165804 1
< 0.1%
64495408 1
< 0.1%
62104453 1
< 0.1%
61362061 1
< 0.1%
59257458 1
< 0.1%

Total Time
Real number (ℝ)

HIGH CORRELATION 

Distinct655
Distinct (%)7.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean248850.88
Minimum8000
Maximum3601000
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size65.1 KiB
2023-11-04T02:16:52.388679image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum8000
5-th percentile113000
Q1183000
median227000
Q3283000
95-th percentile446000
Maximum3601000
Range3593000
Interquartile range (IQR)100000

Descriptive statistics

Standard deviation130264.62
Coefficient of variation (CV)0.52346457
Kurtosis104.47253
Mean248850.88
Median Absolute Deviation (MAD)49000
Skewness6.2826988
Sum2.070937 × 109
Variance1.696887 × 1010
MonotonicityNot monotonic
2023-11-04T02:16:52.441070image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
227000 65
 
0.8%
215000 63
 
0.8%
209000 63
 
0.8%
206000 61
 
0.7%
213000 61
 
0.7%
201000 60
 
0.7%
191000 59
 
0.7%
229000 58
 
0.7%
252000 58
 
0.7%
217000 57
 
0.7%
Other values (645) 7717
92.7%
ValueCountFrequency (%)
8000 1
< 0.1%
12000 1
< 0.1%
16000 1
< 0.1%
20000 1
< 0.1%
22000 1
< 0.1%
23000 1
< 0.1%
24000 2
< 0.1%
26000 1
< 0.1%
27000 2
< 0.1%
28000 2
< 0.1%
ValueCountFrequency (%)
3601000 1
< 0.1%
3107000 1
< 0.1%
2238000 1
< 0.1%
1945000 2
< 0.1%
1656000 1
< 0.1%
1612000 1
< 0.1%
1571000 1
< 0.1%
1424000 1
< 0.1%
1408000 1
< 0.1%
1359000 1
< 0.1%

Track Number
Real number (ℝ)

MISSING 

Distinct139
Distinct (%)1.7%
Missing319
Missing (%)3.8%
Infinite0
Infinite (%)0.0%
Mean19.44121
Minimum1
Maximum210
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size65.1 KiB
2023-11-04T02:16:52.497272image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q14
median8
Q315
95-th percentile92
Maximum210
Range209
Interquartile range (IQR)11

Descriptive statistics

Standard deviation28.340613
Coefficient of variation (CV)1.4577598
Kurtosis4.7962326
Mean19.44121
Median Absolute Deviation (MAD)5
Skewness2.2495896
Sum155588
Variance803.19034
MonotonicityNot monotonic
2023-11-04T02:16:52.545664image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1 582
 
7.0%
3 559
 
6.7%
2 558
 
6.7%
4 527
 
6.3%
5 517
 
6.2%
6 499
 
6.0%
7 482
 
5.8%
8 455
 
5.5%
9 434
 
5.2%
10 403
 
4.8%
Other values (129) 2987
35.9%
ValueCountFrequency (%)
1 582
7.0%
2 558
6.7%
3 559
6.7%
4 527
6.3%
5 517
6.2%
6 499
6.0%
7 482
5.8%
8 455
5.5%
9 434
5.2%
10 403
4.8%
ValueCountFrequency (%)
210 1
< 0.1%
209 1
< 0.1%
208 1
< 0.1%
207 1
< 0.1%
206 1
< 0.1%
205 1
< 0.1%
204 1
< 0.1%
203 1
< 0.1%
201 1
< 0.1%
130 1
< 0.1%

Year
Real number (ℝ)

Distinct54
Distinct (%)0.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2002.0806
Minimum1950
Maximum2015
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size65.1 KiB
2023-11-04T02:16:52.595010image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum1950
5-th percentile1979
Q11999
median2006
Q32008
95-th percentile2011
Maximum2015
Range65
Interquartile range (IQR)9

Descriptive statistics

Standard deviation9.3949188
Coefficient of variation (CV)0.0046925776
Kurtosis2.7121402
Mean2002.0806
Median Absolute Deviation (MAD)3
Skewness-1.7109625
Sum16661315
Variance88.264499
MonotonicityNot monotonic
2023-11-04T02:16:52.863795image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
2008 1630
19.6%
2007 736
 
8.8%
2006 605
 
7.3%
2009 563
 
6.8%
2005 475
 
5.7%
2004 383
 
4.6%
2003 299
 
3.6%
2002 298
 
3.6%
2001 293
 
3.5%
1999 224
 
2.7%
Other values (44) 2816
33.8%
ValueCountFrequency (%)
1950 1
 
< 0.1%
1951 1
 
< 0.1%
1964 2
 
< 0.1%
1965 1
 
< 0.1%
1966 2
 
< 0.1%
1967 14
 
0.2%
1968 12
 
0.1%
1969 48
0.6%
1970 38
0.5%
1971 27
0.3%
ValueCountFrequency (%)
2015 11
 
0.1%
2014 205
 
2.5%
2013 91
 
1.1%
2012 22
 
0.3%
2011 154
 
1.9%
2010 218
 
2.6%
2009 563
 
6.8%
2008 1630
19.6%
2007 736
8.8%
2006 605
 
7.3%

Bit Rate
Real number (ℝ)

HIGH CORRELATION 

Distinct220
Distinct (%)2.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean231.19106
Minimum32
Maximum1553
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size65.1 KiB
2023-11-04T02:16:52.916816image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum32
5-th percentile128
Q1192
median221
Q3272
95-th percentile320
Maximum1553
Range1521
Interquartile range (IQR)80

Descriptive statistics

Standard deviation77.533384
Coefficient of variation (CV)0.33536497
Kurtosis52.80584
Mean231.19106
Median Absolute Deviation (MAD)35
Skewness4.3685951
Sum1923972
Variance6011.4256
MonotonicityNot monotonic
2023-11-04T02:16:52.966615image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
320 1690
20.3%
192 1658
19.9%
128 887
 
10.7%
256 528
 
6.3%
160 142
 
1.7%
224 88
 
1.1%
230 51
 
0.6%
221 48
 
0.6%
246 42
 
0.5%
218 42
 
0.5%
Other values (210) 3146
37.8%
ValueCountFrequency (%)
32 2
 
< 0.1%
56 1
 
< 0.1%
64 1
 
< 0.1%
76 1
 
< 0.1%
80 1
 
< 0.1%
84 1
 
< 0.1%
94 1
 
< 0.1%
96 32
0.4%
106 1
 
< 0.1%
112 17
0.2%
ValueCountFrequency (%)
1553 1
< 0.1%
1367 1
< 0.1%
1364 1
< 0.1%
1349 1
< 0.1%
1318 1
< 0.1%
1264 1
< 0.1%
1037 1
< 0.1%
1035 1
< 0.1%
1025 1
< 0.1%
1020 1
< 0.1%

Track Count
Real number (ℝ)

MISSING 

Distinct21
Distinct (%)1.6%
Missing6971
Missing (%)83.8%
Infinite0
Infinite (%)0.0%
Mean6.2368616
Minimum1
Maximum100
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size65.1 KiB
2023-11-04T02:16:53.015873image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q11
median1
Q311
95-th percentile16
Maximum100
Range99
Interquartile range (IQR)10

Descriptive statistics

Standard deviation7.4030126
Coefficient of variation (CV)1.1869772
Kurtosis50.195485
Mean6.2368616
Median Absolute Deviation (MAD)0
Skewness4.5086408
Sum8426
Variance54.804596
MonotonicityNot monotonic
2023-11-04T02:16:53.056036image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=21)
ValueCountFrequency (%)
1 692
 
8.3%
11 94
 
1.1%
15 93
 
1.1%
10 82
 
1.0%
2 65
 
0.8%
13 56
 
0.7%
9 56
 
0.7%
12 53
 
0.6%
14 38
 
0.5%
18 23
 
0.3%
Other values (11) 99
 
1.2%
(Missing) 6971
83.8%
ValueCountFrequency (%)
1 692
8.3%
2 65
 
0.8%
4 5
 
0.1%
5 6
 
0.1%
6 7
 
0.1%
7 14
 
0.2%
8 10
 
0.1%
9 56
 
0.7%
10 82
 
1.0%
11 94
 
1.1%
ValueCountFrequency (%)
100 2
 
< 0.1%
93 1
 
< 0.1%
33 1
 
< 0.1%
20 20
 
0.2%
18 23
 
0.3%
17 17
 
0.2%
16 16
 
0.2%
15 93
1.1%
14 38
0.5%
13 56
0.7%

Composer
Text

MISSING 

Distinct493
Distinct (%)24.3%
Missing6297
Missing (%)75.7%
Memory size65.1 KiB
2023-11-04T02:16:53.277436image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Length

Max length160
Median length75
Mean length26.282469
Min length3

Characters and Unicode

Total characters53222
Distinct characters83
Distinct categories6 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique289 ?
Unique (%)14.3%

Sample

1st rowC Stelner
2nd rowJohn Debney/Lisbeth Scott
3rd rowJohn Debney
4th rowJohn Debney
5th rowgeorge.ortha@ferialaw.com Feria Tantoco Robeniol Law Offices
ValueCountFrequency (%)
george.ortha@ferialaw.com 200
 
3.1%
law 200
 
3.1%
offices 200
 
3.1%
feria 200
 
3.1%
robeniol 200
 
3.1%
tantoco 200
 
3.1%
harris 94
 
1.5%
83
 
1.3%
diamond 73
 
1.1%
the 71
 
1.1%
Other values (983) 4940
76.5%
2023-11-04T02:16:53.592129image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
e 4740
 
8.9%
4436
 
8.3%
a 4150
 
7.8%
o 3794
 
7.1%
n 3296
 
6.2%
i 3254
 
6.1%
r 3190
 
6.0%
l 1960
 
3.7%
t 1909
 
3.6%
s 1513
 
2.8%
Other values (73) 20980
39.4%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 38362
72.1%
Uppercase Letter 7896
 
14.8%
Space Separator 4436
 
8.3%
Other Punctuation 2436
 
4.6%
Dash Punctuation 75
 
0.1%
Decimal Number 17
 
< 0.1%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
e 4740
12.4%
a 4150
10.8%
o 3794
9.9%
n 3296
 
8.6%
i 3254
 
8.5%
r 3190
 
8.3%
l 1960
 
5.1%
t 1909
 
5.0%
s 1513
 
3.9%
c 1340
 
3.5%
Other values (27) 9216
24.0%
Uppercase Letter
ValueCountFrequency (%)
R 673
 
8.5%
M 603
 
7.6%
T 568
 
7.2%
L 559
 
7.1%
D 533
 
6.8%
B 481
 
6.1%
S 472
 
6.0%
J 457
 
5.8%
O 365
 
4.6%
P 360
 
4.6%
Other values (19) 2825
35.8%
Other Punctuation
ValueCountFrequency (%)
/ 1155
47.4%
. 666
27.3%
@ 237
 
9.7%
, 155
 
6.4%
" 128
 
5.3%
& 53
 
2.2%
' 25
 
1.0%
; 10
 
0.4%
? 4
 
0.2%
: 2
 
0.1%
Decimal Number
ValueCountFrequency (%)
4 13
76.5%
0 2
 
11.8%
1 1
 
5.9%
9 1
 
5.9%
Space Separator
ValueCountFrequency (%)
4436
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 75
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 46258
86.9%
Common 6964
 
13.1%

Most frequent character per script

Latin
ValueCountFrequency (%)
e 4740
 
10.2%
a 4150
 
9.0%
o 3794
 
8.2%
n 3296
 
7.1%
i 3254
 
7.0%
r 3190
 
6.9%
l 1960
 
4.2%
t 1909
 
4.1%
s 1513
 
3.3%
c 1340
 
2.9%
Other values (56) 17112
37.0%
Common
ValueCountFrequency (%)
4436
63.7%
/ 1155
 
16.6%
. 666
 
9.6%
@ 237
 
3.4%
, 155
 
2.2%
" 128
 
1.8%
- 75
 
1.1%
& 53
 
0.8%
' 25
 
0.4%
4 13
 
0.2%
Other values (7) 21
 
0.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 52935
99.5%
None 287
 
0.5%

Most frequent character per block

ASCII
ValueCountFrequency (%)
e 4740
 
9.0%
4436
 
8.4%
a 4150
 
7.8%
o 3794
 
7.2%
n 3296
 
6.2%
i 3254
 
6.1%
r 3190
 
6.0%
l 1960
 
3.7%
t 1909
 
3.6%
s 1513
 
2.9%
Other values (59) 20693
39.1%
None
ValueCountFrequency (%)
é 48
16.7%
á 46
16.0%
ô 42
14.6%
ó 37
12.9%
â 28
9.8%
Ò 18
 
6.3%
È 18
 
6.3%
ñ 13
 
4.5%
ã 12
 
4.2%
ö 10
 
3.5%
Other values (4) 15
 
5.2%

Publisher
Text

MISSING 

Distinct228
Distinct (%)13.3%
Missing6614
Missing (%)79.5%
Memory size65.1 KiB
2023-11-04T02:16:53.779972image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Length

Max length35
Median length21
Mean length10.146956
Min length1

Characters and Unicode

Total characters17331
Distinct characters68
Distinct categories8 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique110 ?
Unique (%)6.4%

Sample

1st rowNapalm /spv
2nd rowSony Music/Integrity Music
3rd rowNIT
4th rowAdeline
5th rowAdeline
ValueCountFrequency (%)
records 173
 
6.3%
emi 147
 
5.4%
nuclear 112
 
4.1%
blast 112
 
4.1%
roadrunner 80
 
2.9%
warner 69
 
2.5%
sanctuary 68
 
2.5%
sony 67
 
2.5%
metal 67
 
2.5%
blade 66
 
2.4%
Other values (269) 1765
64.7%
2023-11-04T02:16:54.013203image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
a 1587
 
9.2%
e 1408
 
8.1%
r 1262
 
7.3%
o 1020
 
5.9%
1018
 
5.9%
n 1015
 
5.9%
s 901
 
5.2%
l 882
 
5.1%
i 744
 
4.3%
t 722
 
4.2%
Other values (58) 6772
39.1%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 12731
73.5%
Uppercase Letter 3241
 
18.7%
Space Separator 1018
 
5.9%
Other Punctuation 163
 
0.9%
Decimal Number 84
 
0.5%
Close Punctuation 37
 
0.2%
Open Punctuation 37
 
0.2%
Dash Punctuation 20
 
0.1%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
R 409
12.6%
M 361
11.1%
B 331
10.2%
I 294
 
9.1%
E 236
 
7.3%
S 228
 
7.0%
N 185
 
5.7%
C 158
 
4.9%
W 121
 
3.7%
T 118
 
3.6%
Other values (16) 800
24.7%
Lowercase Letter
ValueCountFrequency (%)
a 1587
12.5%
e 1408
11.1%
r 1262
9.9%
o 1020
8.0%
n 1015
8.0%
s 901
 
7.1%
l 882
 
6.9%
i 744
 
5.8%
t 722
 
5.7%
d 654
 
5.1%
Other values (15) 2536
19.9%
Other Punctuation
ValueCountFrequency (%)
. 67
41.1%
/ 67
41.1%
@ 13
 
8.0%
' 13
 
8.0%
! 2
 
1.2%
& 1
 
0.6%
Decimal Number
ValueCountFrequency (%)
4 25
29.8%
0 24
28.6%
2 14
16.7%
9 12
14.3%
5 9
 
10.7%
Close Punctuation
ValueCountFrequency (%)
) 22
59.5%
] 15
40.5%
Open Punctuation
ValueCountFrequency (%)
( 22
59.5%
[ 15
40.5%
Space Separator
ValueCountFrequency (%)
1018
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 20
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 15972
92.2%
Common 1359
 
7.8%

Most frequent character per script

Latin
ValueCountFrequency (%)
a 1587
 
9.9%
e 1408
 
8.8%
r 1262
 
7.9%
o 1020
 
6.4%
n 1015
 
6.4%
s 901
 
5.6%
l 882
 
5.5%
i 744
 
4.7%
t 722
 
4.5%
d 654
 
4.1%
Other values (41) 5777
36.2%
Common
ValueCountFrequency (%)
1018
74.9%
. 67
 
4.9%
/ 67
 
4.9%
4 25
 
1.8%
0 24
 
1.8%
) 22
 
1.6%
( 22
 
1.6%
- 20
 
1.5%
[ 15
 
1.1%
] 15
 
1.1%
Other values (7) 64
 
4.7%

Most occurring blocks

ValueCountFrequency (%)
ASCII 17331
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
a 1587
 
9.2%
e 1408
 
8.1%
r 1262
 
7.3%
o 1020
 
5.9%
1018
 
5.9%
n 1015
 
5.9%
s 901
 
5.2%
l 882
 
5.1%
i 744
 
4.3%
t 722
 
4.2%
Other values (58) 6772
39.1%

Date Modified
Date

Distinct4458
Distinct (%)53.6%
Missing0
Missing (%)0.0%
Memory size65.1 KiB
Minimum2004-10-08 02:47:57+00:00
Maximum2020-11-17 07:05:16+00:00
2023-11-04T02:16:54.087599image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-11-04T02:16:54.141369image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

Date Added
Date

Distinct562
Distinct (%)6.8%
Missing0
Missing (%)0.0%
Memory size65.1 KiB
Minimum2023-10-23 02:03:07+00:00
Maximum2023-10-23 02:13:52+00:00
2023-11-04T02:16:54.191778image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-11-04T02:16:54.242733image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

Interactions

2023-11-04T02:16:49.355539image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-11-04T02:16:47.298813image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-11-04T02:16:47.720773image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-11-04T02:16:48.060573image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-11-04T02:16:48.397831image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-11-04T02:16:48.708115image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-11-04T02:16:49.029841image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-11-04T02:16:49.396510image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-11-04T02:16:47.368102image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-11-04T02:16:47.770666image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-11-04T02:16:48.109765image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-11-04T02:16:48.442336image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-11-04T02:16:48.754365image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-11-04T02:16:49.076350image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-11-04T02:16:49.438452image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-11-04T02:16:47.447654image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-11-04T02:16:47.819509image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-11-04T02:16:48.158257image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-11-04T02:16:48.482564image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-11-04T02:16:48.802282image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-11-04T02:16:49.125793image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-11-04T02:16:49.485769image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-11-04T02:16:47.526959image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-11-04T02:16:47.870656image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-11-04T02:16:48.207652image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-11-04T02:16:48.529180image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-11-04T02:16:48.850246image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-11-04T02:16:49.174216image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-11-04T02:16:49.527491image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-11-04T02:16:47.581038image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-11-04T02:16:47.913602image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-11-04T02:16:48.250552image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-11-04T02:16:48.570255image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-11-04T02:16:48.891869image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-11-04T02:16:49.219212image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-11-04T02:16:49.570911image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-11-04T02:16:47.626971image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-11-04T02:16:47.964336image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-11-04T02:16:48.298661image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-11-04T02:16:48.617566image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-11-04T02:16:48.937996image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-11-04T02:16:49.265756image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-11-04T02:16:49.610954image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-11-04T02:16:47.677063image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-11-04T02:16:48.015214image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-11-04T02:16:48.348140image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-11-04T02:16:48.664545image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-11-04T02:16:48.985536image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-11-04T02:16:49.314223image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Correlations

2023-11-04T02:16:54.285742image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Track IDSizeTotal TimeTrack NumberYearBit RateTrack Count
Track ID1.0000.012-0.0810.2500.1990.1370.095
Size0.0121.0000.756-0.0390.0520.5470.041
Total Time-0.0810.7561.000-0.139-0.096-0.0480.074
Track Number0.250-0.039-0.1391.0000.3600.092-0.061
Year0.1990.052-0.0960.3601.0000.179-0.110
Bit Rate0.1370.547-0.0480.0920.1791.000-0.039
Track Count0.0950.0410.074-0.061-0.110-0.0391.000

Missing values

2023-11-04T02:16:49.677701image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
A simple visualization of nullity by column.
2023-11-04T02:16:49.777458image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.
2023-11-04T02:16:49.857621image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
The correlation heatmap measures nullity correlation: how strongly the presence or absence of one variable affects the presence of another.

Sample

Track IDNameArtistAlbum ArtistAlbumGenreSizeTotal TimeTrack NumberYearBit RateTrack CountComposerPublisherDate ModifiedDate Added
00A Story Of Boy Meets GirlMychael Danna & Rob SimonsenMychael Danna & Rob Simonsen(500) Days Of SummerSoundtrack1529041950001.0200912816.0NoneNone2009-11-06T05:43:50Z2023-10-23T02:03:07Z
11UsRegina SpektorRegina Spektor(500) Days Of SummerSoundtrack46402932890002.0200912816.0NoneNone2009-11-06T05:43:50Z2023-10-23T02:03:07Z
22There Is A Light That Never Goes OutThe SmithsThe Smiths(500) Days Of SummerSoundtrack39047142430003.0200912816.0NoneNone2009-11-06T05:43:49Z2023-10-23T02:03:07Z
33Bad KidsBlack LipsBlack Lips(500) Days Of SummerSoundtrack20564711280004.0200912816.0NoneNone2009-11-06T05:43:48Z2023-10-23T02:03:07Z
44Please, Please, Please, Let Me Get What I WantThe SmithsThe Smiths(500) Days Of SummerSoundtrack18053151120005.0200912816.0NoneNone2009-11-06T05:43:48Z2023-10-23T02:03:07Z
55There Goes The FearDovesDoves(500) Days Of SummerSoundtrack66586264160006.0200912816.0NoneNone2009-11-06T05:43:47Z2023-10-23T02:03:07Z
66You Make My DreamsHall & OatesHall & Oates(500) Days Of SummerSoundtrack29693061850007.0200912816.0NoneNone2009-11-06T05:43:49Z2023-10-23T02:03:08Z
77Sweet DispositionThe Temper TrapThe Temper Trap(500) Days Of SummerSoundtrack37375172330008.0200912816.0NoneNone2009-11-06T05:43:50Z2023-10-23T02:03:08Z
88Quequ'un M'a DitCarla BruniCarla Bruni(500) Days Of SummerSoundtrack26307561640009.0200912816.0NoneNone2009-11-06T05:43:48Z2023-10-23T02:03:08Z
99MushaboomFeistFeist(500) Days Of SummerSoundtrack359664822400010.0200912816.0NoneNone2009-11-06T05:43:34Z2023-10-23T02:03:08Z
Track IDNameArtistAlbum ArtistAlbumGenreSizeTotal TimeTrack NumberYearBit RateTrack CountComposerPublisherDate ModifiedDate Added
831211425Nada (Sebastian Tellier)ZoéZoéReptilectric RevisitadoAlt Rock36059441820002.02009146NaNLeon LarreguiNone2009-11-08T08:40:05Z2023-10-23T02:13:52Z
831311426Neandertal (Bufi)ZoéZoéReptilectric RevisitadoAlt Rock60594593140001.02009147NaNLeon LarreguiNone2009-11-08T08:40:09Z2023-10-23T02:13:52Z
831411427No Hay Dolor (Vitamins For You)ZoéZoéReptilectric RevisitadoAlt Rock1086891762500012.02009135NaNLeon LarreguiNone2009-11-08T08:40:18Z2023-10-23T02:13:52Z
831511428Nothing (Nick Mccarthy)ZoéZoéReptilectric RevisitadoAlt Rock38877671980009.02009146NaNLeon LarreguiNone2009-11-08T08:40:22Z2023-10-23T02:13:52Z
831611429Poli (Schneider TM)ZoéZoéReptilectric RevisitadoAlt Rock40731702010003.02009151NaNLeon LarreguiNone2009-11-08T08:40:24Z2023-10-23T02:13:52Z
831711430Reptilectric (Dramian & Luriel)ZoéZoéReptilectric RevisitadoAlt Rock69838153530008.02009152NaNLeon LarreguiNone2009-11-08T08:40:28Z2023-10-23T02:13:52Z
831811431Resiste (Colder)ZoéZoéReptilectric RevisitadoAlt Rock65837023300004.02009152NaNLeon LarreguiNone2009-11-08T08:40:31Z2023-10-23T02:13:52Z
831911432Sombras (The Glimmers)ZoéZoéReptilectric RevisitadoAlt Rock683236537100013.02009141NaNLeon LarreguiNone2009-11-08T08:40:35Z2023-10-23T02:13:52Z
832011433Sombras (Yamil Rezc)ZoéZoéReptilectric RevisitadoAlt Rock53523732920005.02009139NaNLeon LarreguiNone2009-11-08T08:40:39Z2023-10-23T02:13:52Z
832111434Ultimos Dias (Panico)ZoéZoéReptilectric RevisitadoAlt Rock76395424050007.02009145NaNLeon LarreguiNone2009-11-08T08:40:43Z2023-10-23T02:13:52Z